High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra

Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km2). We used these field data together with high-resolution remote sensing data to develop machine learning models for predicting (i.e., upscaling) daytime GHG fluxes across the landscape at 2 m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO2 uptake, it also revealed small but widespread CH4 uptake in upland vegetation types, almost surpassing the high wetland CH4 emissions at the landscape scale. Average N2O fluxes were negligible. CO2 fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH4 fluxes by soil moisture (increases net emissions) and vegetation type, and N2O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modeling of GHG fluxes in the Arctic. They also reveal the dominant role of CO2 fluxes across the tundra landscape but suggest that CH4 uptake in dry upland soils might play a significant role in the regional GHG budget.</p

Cyclin A as a marker for prognosis and chemotherapy response in advanced breast cancer

We wanted to study cyclin A as a marker for prognosis and chemotherapy response. A total of 283 women with metastatic breast cancer were initially enrolled in a randomised multicentre trial comparing docetaxel to sequential methotrexate-fluorouracil (MF) in advanced breast cancer after anthracycline failure. Paraffin-embedded blocks of the primary tumour were available for 96 patients (34%). The proportion of cells expressing cyclin A was determined by immunohistochemistry using a mouse monoclonal antibody to human cyclin A. Response evaluation was performed according to WHO recommendations. The median cyclin A positivity of tumour cells was 14.5% (range 1.2–45.0). Cyclin A correlated statistically significantly to all other tested proliferation markers (mitotic count, histological grade and Ki-67). A high cyclin A correlated significantly to a shorter time to first relapse, risk ratio (RR) 1.94 (95% CI 1.24–3.03) and survival from diagnosis, RR 2.49 (95% CI 1.45–4.29), cutoff point for high/low proliferation group 10.5%. Cyclin A did not correlate to chemotherapy response or survival after anthracycline, docetaxel or MF therapy. Of all tumour biological factors tested (mitotic count, histological grade and Ki-67), cyclin A seemed to have the strongest prognostic value. Cyclin A is a good marker for tumour proliferation and prognosis in breast cancer. In the present study, cyclin A did not predict chemotherapy response

Risk Predictors and Symptom Features of Long COVID Within a Broad Primary Care Patient Population Including Both Tested and Untested Patients

Introduction: Symptoms may persist after the initial phases of COVID-19 infection, a phenomenon termed long COVID. Current knowledge on long COVID has been mostly derived from test-confirmed and hospitalized COVID-19 patients. Data are required on the burden and predictors of long COVID in a broader patient group, which includes both tested and untested COVID-19 patients in primary care. Methods: This is an observational study using data from Platform C19, a quality improvement program-derived research database linking primary care electronic health record data (EHR) with patient-reported questionnaire information. Participating general practices invited consenting patients aged 18– 85 to complete an online questionnaire since 7th August 2020. COVID-19 self-diagnosis, clinician-diagnosis, testing, and the presence and duration of symptoms were assessed via the questionnaire. Patients were considered present with long COVID if they reported symptoms lasting ≥ 4 weeks. EHR and questionnaire data up till 22nd January 2021 were extracted for analysis. Multivariable regression analyses were conducted comparing demographics, clinical characteristics, and presence of symptoms between patients with long COVID and patients with shorter symptom duration. Results: Long COVID was present in 310/3151 (9.8%) patients with self-diagnosed, clinician-diagnosed, or test-confirmed COVID-19. Only 106/310 (34.2%) long COVID patients had test-confirmed COVID-19. Risk predictors of long COVID were age ≥ 40 years (adjusted Odds Ratio [AdjOR]=1.49 [1.05– 2.17]), female sex (adjOR=1.37 [1.02– 1.85]), frailty (adjOR=2.39 [1.29– 4.27]), visit to A&E (adjOR=4.28 [2.31– 7.78]), and hospital admission for COVID-19 symptoms (adjOR=3.22 [1.77– 5.79]). Aches and pain (adjOR=1.70 [1.21– 2.39]), appetite loss (adjOR=3.15 [1.78– 5.92]), confusion and disorientation (adjOR=2.17 [1.57– 2.99]), diarrhea (adjOR=1.4 [1.03– 1.89]), and persistent dry cough (adjOR=2.77 [1.94– 3.98]) were symptom features statistically more common in long COVID. Conclusion: This study reports the factors and symptom features predicting long COVID in a broad primary care population, including both test-confirmed and the previously missed group of COVID-19 patients

Links between maternal postpartum depressive symptoms, maternal distress, infant gender and sensitivity in a high-risk population

<p>Abstract</p> <p>Background</p> <p>Maternal postpartum depression has an impact on mother-infant interaction. Mothers with depression display less positive affect and sensitivity in interaction with their infants compared to non-depressed mothers. Depressed women also show more signs of distress and difficulties adjusting to their role as mothers than non-depressed women. In addition, depressive mothers are reported to be affectively more negative with their sons than with daughters.</p> <p>Methods</p> <p>A non-clinical sample of 106 mother-infant dyads at psychosocial risk (poverty, alcohol or drug abuse, lack of social support, teenage mothers and maternal psychic disorder) was investigated with EPDS (maternal postpartum depressive symptoms), the CARE-Index (maternal sensitivity in a dyadic context) and PSI-SF (maternal distress). The baseline data were collected when the babies had reached 19 weeks of age.</p> <p>Results</p> <p>A hierarchical regression analysis yielded a highly significant relation between the PSI-SF subscale "parental distress" and the EPDS total score, accounting for 55% of the variance in the EPDS. The other variables did not significantly predict the severity of depressive symptoms. A two-way ANOVA with "infant gender" and "maternal postpartum depressive symptoms" showed no interaction effect on maternal sensitivity.</p> <p>Conclusions</p> <p>Depressive symptoms and maternal sensitivity were not linked. It is likely that we could not find any relation between both variables due to different measuring methods (self-reporting and observation). Maternal distress was strongly related to maternal depressive symptoms, probably due to the generally increased burden in the sample, and contributed to 55% of the variance of postpartum depressive symptoms.</p

Climatic predictors of species distributions neglect biophysiologically meaningful variables

This is the final version. Available on open access from Wiley via the DOI in this record.Aim: Species distribution models (SDMs) have played a pivotal role in predicting how species might respond to climate change. To generate reliable and realistic predictions from these models requires the use of climate variables that adequately capture physiological responses of species to climate and therefore provide a proximal link between climate and their distributions. Here, we examine whether the climate variables used in plant SDMs are different from those known to influence directly plant physiology. Location: Global. Methods: We carry out an extensive, systematic review of the climate variables used to model the distributions of plant species and provide comparison to the climate variables identified as important in the plant physiology literature. We calculate the top ten SDM and physiology variables at 2.5 degree spatial resolution for the globe and use principal component analyses and multiple regression to assess similarity between the climatic variation described by both variable sets. Results: We find that the most commonly used SDM variables do not reflect the most important physiological variables and differ in two main ways: (i) SDM variables rely on seasonal or annual rainfall as simple proxies of water available to plants and neglect more direct measures such as soil water content; and (ii) SDM variables are typically averaged across seasons or years and overlook the importance of climatic events within the critical growth period of plants. We identify notable differences in their spatial gradients globally and show where distal variables may be less reliable proxies for the variables to which species are known to respond. Main conclusions: There is a growing need for the development of accessible, fine-resolution global climate surfaces of physiological variables. This would provide a means to improve the reliability of future range predictions from SDMs and support efforts to conserve biodiversity in a changing climate

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2&nbsp;m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315&nbsp;cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\ub0C (mean&nbsp;=&nbsp;3.0&nbsp;\ub1&nbsp;2.1\ub0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6&nbsp;\ub1&nbsp;2.3\ub0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler ( 120.7&nbsp;\ub1&nbsp;2.3\ub0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Effects of Climate and Atmospheric Nitrogen Deposition on Early to Mid-Term Stage Litter Decomposition Across Biomes

open263siWe acknowledge support by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118), Scientific Grant Agency VEGA(GrantNo.2/0101/18), as well as by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement No. 677232)Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of low-quality litter by 0.9-1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate.openKwon T.; Shibata H.; Kepfer-Rojas S.; Schmidt I.K.; Larsen K.S.; Beier C.; Berg B.; Verheyen K.; Lamarque J.-F.; Hagedorn F.; Eisenhauer N.; Djukic I.; Caliman A.; Paquette A.; Gutierrez-Giron A.; Petraglia A.; Augustaitis A.; Saillard A.; Ruiz-Fernandez A.C.; Sousa A.I.; Lillebo A.I.; Da Rocha Gripp A.; Lamprecht A.; Bohner A.; Francez A.-J.; Malyshev A.; Andric A.; Stanisci A.; Zolles A.; Avila A.; Virkkala A.-M.; Probst A.; Ouin A.; Khuroo A.A.; Verstraeten A.; Stefanski A.; Gaxiola A.; Muys B.; Gozalo B.; Ahrends B.; Yang B.; Erschbamer B.; Rodriguez Ortiz C.E.; Christiansen C.T.; Meredieu C.; Mony C.; Nock C.; Wang C.-P.; Baum C.; Rixen C.; Delire C.; Piscart C.; Andrews C.; Rebmann C.; Branquinho C.; Jan D.; Wundram D.; Vujanovic D.; Adair E.C.; Ordonez-Regil E.; Crawford E.R.; Tropina E.F.; Hornung E.; Groner E.; Lucot E.; Gacia E.; Levesque E.; Benedito E.; Davydov E.A.; Bolzan F.P.; Maestre F.T.; Maunoury-Danger F.; Kitz F.; Hofhansl F.; Hofhansl G.; De Almeida Lobo F.; Souza F.L.; Zehetner F.; Koffi F.K.; Wohlfahrt G.; Certini G.; Pinha G.D.; Gonzlez G.; Canut G.; Pauli H.; Bahamonde H.A.; Feldhaar H.; Jger H.; Serrano H.C.; Verheyden H.; Bruelheide H.; Meesenburg H.; Jungkunst H.; Jactel H.; Kurokawa H.; Yesilonis I.; Melece I.; Van Halder I.; Quiros I.G.; Fekete I.; Ostonen I.; Borovsk J.; Roales J.; Shoqeir J.H.; Jean-Christophe Lata J.; Probst J.-L.; Vijayanathan J.; Dolezal J.; Sanchez-Cabeza J.-A.; Merlet J.; Loehr J.; Von Oppen J.; Loffler J.; Benito Alonso J.L.; Cardoso-Mohedano J.-G.; Penuelas J.; Morina J.C.; Quinde J.D.; Jimnez J.J.; Alatalo J.M.; Seeber J.; Kemppinen J.; Stadler J.; Kriiska K.; Van Den Meersche K.; Fukuzawa K.; Szlavecz K.; Juhos K.; Gerhtov K.; Lajtha K.; Jennings K.; Jennings J.; Ecology P.; Hoshizaki K.; Green K.; Steinbauer K.; Pazianoto L.; Dienstbach L.; Yahdjian L.; Williams L.J.; Brigham L.; Hanna L.; Hanna H.; Rustad L.; Morillas L.; Silva Carneiro L.; Di Martino L.; Villar L.; Fernandes Tavares L.A.; Morley M.; Winkler M.; Lebouvier M.; Tomaselli M.; Schaub M.; Glushkova M.; Torres M.G.A.; De Graaff M.-A.; Pons M.-N.; Bauters M.; Mazn M.; Frenzel M.; Wagner M.; Didion M.; Hamid M.; Lopes M.; Apple M.; Weih M.; Mojses M.; Gualmini M.; Vadeboncoeur M.; Bierbaumer M.; Danger M.; Scherer-Lorenzen M.; Ruek M.; Isabellon M.; Di Musciano M.; Carbognani M.; Zhiyanski M.; Puca M.; Barna M.; Ataka M.; Luoto M.; H. Alsafaran M.; Barsoum N.; Tokuchi N.; Korboulewsky N.; Lecomte N.; Filippova N.; Hlzel N.; Ferlian O.; Romero O.; Pinto-Jr O.; Peri P.; Dan Turtureanu P.; Haase P.; Macreadie P.; Reich P.B.; Petk P.; Choler P.; Marmonier P.; Ponette Q.; Dettogni Guariento R.; Canessa R.; Kiese R.; Hewitt R.; Weigel R.; Kanka R.; Cazzolla Gatti R.; Martins R.L.; Ogaya R.; Georges R.; Gaviln R.G.; Wittlinger S.; Puijalon S.; Suzuki S.; Martin S.; Anja S.; Gogo S.; Schueler S.; Drollinger S.; Mereu S.; Wipf S.; Trevathan-Tackett S.; Stoll S.; Lfgren S.; Trogisch S.; Seitz S.; Glatzel S.; Venn S.; Dousset S.; Mori T.; Sato T.; Hishi T.; Nakaji T.; Jean-Paul T.; Camboulive T.; Spiegelberger T.; Scholten T.; Mozdzer T.J.; Kleinebecker T.; Runk T.; Ramaswiela T.; Hiura T.; Enoki T.; Ursu T.-M.; Di Cella U.M.; Hamer U.; Klaus V.; Di Cecco V.; Rego V.; Fontana V.; Piscov V.; Bretagnolle V.; Maire V.; Farjalla V.; Pascal V.; Zhou W.; Luo W.; Parker W.; Parker P.; Kominam Y.; Kotrocz Z.; Utsumi Y.Kwon T.; Shibata H.; Kepfer-Rojas S.; Schmidt I.K.; Larsen K.S.; Beier C.; Berg B.; Verheyen K.; Lamarque J.-F.; Hagedorn F.; Eisenhauer N.; Djukic I.; Caliman A.; Paquette A.; Gutierrez-Giron A.; Petraglia A.; Augustaitis A.; Saillard A.; Ruiz-Fernandez A.C.; Sousa A.I.; Lillebo A.I.; Da Rocha Gripp A.; Lamprecht A.; Bohner A.; Francez A.-J.; Malyshev A.; Andric A.; Stanisci A.; Zolles A.; Avila A.; Virkkala A.-M.; Probst A.; Ouin A.; Khuroo A.A.; Verstraeten A.; Stefanski A.; Gaxiola A.; Muys B.; Gozalo B.; Ahrends B.; Yang B.; Erschbamer B.; Rodriguez Ortiz C.E.; Christiansen C.T.; Meredieu C.; Mony C.; Nock C.; Wang C.-P.; Baum C.; Rixen C.; Delire C.; Piscart C.; Andrews C.; Rebmann C.; Branquinho C.; Jan D.; Wundram D.; Vujanovic D.; Adair E.C.; Ordonez-Regil E.; Crawford E.R.; Tropina E.F.; Hornung E.; Groner E.; Lucot E.; Gacia E.; Levesque E.; Benedito E.; Davydov E.A.; Bolzan F.P.; Maestre F.T.; Maunoury-Danger F.; Kitz F.; Hofhansl F.; Hofhansl G.; De Almeida Lobo F.; Souza F.L.; Zehetner F.; Koffi F.K.; Wohlfahrt G.; Certini G.; Pinha G.D.; Gonzlez G.; Canut G.; Pauli H.; Bahamonde H.A.; Feldhaar H.; Jger H.; Serrano H.C.; Verheyden H.; Bruelheide H.; Meesenburg H.; Jungkunst H.; Jactel H.; Kurokawa H.; Yesilonis I.; Melece I.; Van Halder I.; Quiros I.G.; Fekete I.; Ostonen I.; Borovsk J.; Roales J.; Shoqeir J.H.; Jean-Christophe Lata J.; Probst J.-L.; Vijayanathan J.; Dolezal J.; Sanchez-Cabeza J.-A.; Merlet J.; Loehr J.; Von Oppen J.; Loffler J.; Benito Alonso J.L.; Cardoso-Mohedano J.-G.; Penuelas J.; Morina J.C.; Quinde J.D.; Jimnez J.J.; Alatalo J.M.; Seeber J.; Kemppinen J.; Stadler J.; Kriiska K.; Van Den Meersche K.; Fukuzawa K.; Szlavecz K.; Juhos K.; Gerhtov K.; Lajtha K.; Jennings K.; Jennings J.; Ecology P.; Hoshizaki K.; Green K.; Steinbauer K.; Pazianoto L.; Dienstbach L.; Yahdjian L.; Williams L.J.; Brigham L.; Hanna L.; Hanna H.; Rustad L.; Morillas L.; Silva Carneiro L.; Di Martino L.; Villar L.; Fernandes Tavares L.A.; Morley M.; Winkler M.; Lebouvier M.; Tomaselli M.; Schaub M.; Glushkova M.; Torres M.G.A.; De Graaff M.-A.; Pons M.-N.; Bauters M.; Mazn M.; Frenzel M.; Wagner M.; Didion M.; Hamid M.; Lopes M.; Apple M.; Weih M.; Mojses M.; Gualmini M.; Vadeboncoeur M.; Bierbaumer M.; Danger M.; Scherer-Lorenzen M.; Ruek M.; Isabellon M.; Di Musciano M.; Carbognani M.; Zhiyanski M.; Puca M.; Barna M.; Ataka M.; Luoto M.; H. Alsafaran M.; Barsoum N.; Tokuchi N.; Korboulewsky N.; Lecomte N.; Filippova N.; Hlzel N.; Ferlian O.; Romero O.; Pinto-Jr O.; Peri P.; Dan Turtureanu P.; Haase P.; Macreadie P.; Reich P.B.; Petk P.; Choler P.; Marmonier P.; Ponette Q.; Dettogni Guariento R.; Canessa R.; Kiese R.; Hewitt R.; Weigel R.; Kanka R.; Cazzolla Gatti R.; Martins R.L.; Ogaya R.; Georges R.; Gaviln R.G.; Wittlinger S.; Puijalon S.; Suzuki S.; Martin S.; Anja S.; Gogo S.; Schueler S.; Drollinger S.; Mereu S.; Wipf S.; Trevathan-Tackett S.; Stoll S.; Lfgren S.; Trogisch S.; Seitz S.; Glatzel S.; Venn S.; Dousset S.; Mori T.; Sato T.; Hishi T.; Nakaji T.; Jean-Paul T.; Camboulive T.; Spiegelberger T.; Scholten T.; Mozdzer T.J.; Kleinebecker T.; Runk T.; Ramaswiela T.; Hiura T.; Enoki T.; Ursu T.-M.; Di Cella U.M.; Hamer U.; Klaus V.; Di Cecco V.; Rego V.; Fontana V.; Piscov V.; Bretagnolle V.; Maire V.; Farjalla V.; Pascal V.; Zhou W.; Luo W.; Parker W.; Parker P.; Kominam Y.; Kotrocz Z.; Utsumi Y

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km &lt;sup&gt;2&lt;/sup&gt; resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km &lt;sup&gt;2&lt;/sup&gt; pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications